Synthetic structural magnetic resonance image generator improves deep learning prediction of schizophrenia

Alvaro Ulloa, Sergey Plis, Erik Erhardt, Vince Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Despite the rapidly growing interest, progress in the study of relations between physiological abnormalities and mental disorders is hampered by complexity of the human brain and high costs of data collection. The complexity can be captured by deep learning approaches, but they still may require significant amounts of data. In this paper, we seek to mitigate the latter challenge by developing a generator for synthetic realistic training data. Our method greatly improves generalization in classification of schizophrenia patients and healthy controls from their structural magnetic resonance images. A feed forward neural network trained exclusively on continuously generated synthetic data produces the best area under the curve compared to classifiers trained on real data alone.

Original languageEnglish (US)
Title of host publication2015 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2015
EditorsDeniz Erdogmus, Serdar Kozat, Jan Larsen, Murat Akcakaya
PublisherIEEE Computer Society
ISBN (Electronic)9781467374545
DOIs
StatePublished - Nov 10 2015
Externally publishedYes
Event25th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2015 - Boston, United States
Duration: Sep 17 2015Sep 20 2015

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2015-November
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Other

Other25th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2015
CountryUnited States
CityBoston
Period9/17/159/20/15

Keywords

  • Biological neural networks
  • Generators
  • Machine learning
  • Magnetic resonance imaging
  • Neuroimaging
  • Probability density function
  • Training

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

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